Field of the Invention
The present invention relates to plant control.
Background Art
A control apparatus of a plant calculates an operational control signal for performing operational control of the plant, using a measurement signal that is measured in the plant and a control parameter. In order to operate the plant in such a manner that the plant produces desired performance, there is a need to suitably adjust a setting value of the control parameter that is used when calculating the operational control signal. When multiple control parameters are present, it is difficult to manually search for a setting value of the control parameter for producing the desired performance. For this reason, a technology that automatically makes an optimal adjustment of the setting value of the control parameter according to a purpose is required in a control apparatus of the plant.
Disclosed in JP-A-2009-030476 is a technology in which a result of calculation that uses a dynamic-characteristic simulator is stored in a simulation database and the stored data is learned with a neural network and thus an optimal solution in accordance with a purpose is searched for.
A technology in which, in activation control of a combined cycle power generating plant that is a kind of power generating plants, a setting value of a control parameter of an activation control apparatus is optimally adjusted based on the reinforcement learning theory is disclosed in KAMIYA Akimoto, “Reinforcement Learning Applied to Power Plant Start-Up Scheduling”, The Japanese Society for Artificial Intelligence, Vol. 12, No. 6, P 837-844 (1997/11). In this technology, firstly, a control parameter search range that satisfies an activation restriction condition is determined using a genetic algorithm. Then, a suitable setting value of the suitable control parameter is searched for within the determined control parameter search range using reinforcement learning.
As a technology that makes an optimal adjustment of the setting value of the control parameter according to a control purpose and a restriction condition, there is a learning algorithm that uses the neural network that is disclosed in JP-A-2009-030476, or a learning algorithm that uses the genetic algorithm and the reinforcement learning that are disclosed in KAMIYA Akimoto, “Reinforcement Learning Applied to Power Plant Start-Up Scheduling”, The Japanese Society for Artificial Intelligence, Vol. 12, No. 6, P 837-844 (1997/11). These learning algorithms involve repeated calculation using a simulator that is equipped with a model of a control target when calculating an optimal value of the control parameter.
The calculation time required to optimize the control parameter is in proportion to the control parameter search range. The search range increases exponentially according to the number of types of control parameters to be optimized or the number of divisions of the setting value of the control parameter. Accordingly, as the number of types of control parameters or the number of divisions of the setting values of each control parameter gets larger, the calculation time increases. When performing plant operation, there is also a case where the setting value of the control parameter has to be adjusted within a limited time. Accordingly, in a case where the calculation time for the optimization is long, there is a need to employ ingenuity to shorten the calculation time.
As a method of shortening the calculation time for the optimization of the control parameter, there is a method of reducing the search range. In JP-A-2009-030476, the whole range of values that the setting value of the control parameter can take is set as the search range. Accordingly, in a case where the search range is large, the calculation time gets long. On the other hand, the search range for the reinforcement learning is reduced by searching for the setting value of the control parameter that satisfies the activation restriction condition with the genetic algorithm, using the simulator, in KAMIYA Akimoto, “Reinforcement Learning Applied to Power Plant Start-Up Scheduling”, The Japanese Society for Artificial Intelligence, Vol. 12, No. 6, P 837-844 (1997/11). However, in a case where a calculation load on the simulator is heavy, it takes time to reduce the search range. Accordingly, there is a likelihood that the calculation time for the optimization will not be shortened.